SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 631–640 of 1963 papers

TitleStatusHype
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Explainable Learning with Gaussian ProcessesCode0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Chained Gaussian ProcessesCode0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified